NEWS · RESEARCH · #252
AutoBNN — compositional Bayesian neural networks for probabilistic time-series forecasting (open-source, Google Research)
Google Research (post by Urs Köster) presents AutoBNN, an open-source JAX package available within TensorFlow Probability that replaces Gaussian processes with compositional Bayesian neural networks to automate discovery of interpretable time-series forecasting models, produce uncertainty estimates, and scale more efficiently to large datasets. AutoBNN maps compositional GP kernels to BNN architectures and supports operators analogous to GP addition and multiplication while enabling GPU/TPU acceleration and possible hybrid architectures with deep BNN components.
KEY POINTS
- Google Research (post by Urs Köster) presents AutoBNN, an open-source JAX package available within TensorFlow Probability that replaces Gaussian processes with compositional Bayesian neural networks to automate discovery of interpretable time-series forecasting models, produce uncertainty estimates, and scale more efficiently to large datasets.
- AutoBNN maps compositional GP kernels to BNN architectures and supports operators analogous to GP addition and multiplication while enabling GPU/TPU acceleration and possible hybrid architectures with deep BNN components.
- If AutoBNN performs as described, substituting compositional BNNs for GPs could give practitioners interpretable, GPU-accelerated probabilistic forecasts with better scalability and uncertainty estimates for large or high-dimensional time-series problems.
WHY IT MATTERS
If AutoBNN performs as described, substituting compositional BNNs for GPs could give practitioners interpretable, GPU-accelerated probabilistic forecasts with better scalability and uncertainty estimates for large or high-dimensional time-series problems.